Snow avalanche impact pressure - vulnerability relations for use in risk assessment
Bibliographic record
Abstract
Use of formal risk analysis to assess avalanche danger is currently limited by a lack of knowledge of how avalanche impact pressures damage structures and cause fatalities. That is, the vulnerability component of risk is poorly specified. In this paper we outline a method for deriving vulnerability values as a function of position downslope for a range of avalanche sizes. The method is based on the weighted average of vulnerability and uses an avalanche-dynamics model embedded within a statistical framework. The models seem to behave in a consistent manner. By allowing avalanche size and stopping position to vary and calculating vulnerability as a function of distance from the stopping position, vulnerability values are less approximate than the assumption of a constant vulnerability value for each individual size. When the assumptions underlying the impact pressure - vulnerability relation are perturbed, the results seem to be robust. The method outlined here should provide a way for avalanche experts to reformulate danger zones based on return period and impact pressure so that they are set within a risk framework.Key words: risk, vulnerability, snow, avalanches, impact pressure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".